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Automatic Learning of Gait Signatures for People Identification

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Advances in Computational Intelligence (IWANN 2017)

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This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task, both for identification and gender recognition, with an image resolution eight times lower than the previously reported results (i.e. \(80\times 60\) pixels).

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  1. 1.

    Note that TUM-GAID distinguishes between training/test subjects and training/test sequences. Test sequences are never used for training or validation of the model.


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Correspondence to Francisco Manuel Castro .

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Castro, F.M., Marín-Jiménez, M.J., Guil, N., Pérez de la Blanca, N. (2017). Automatic Learning of Gait Signatures for People Identification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham.

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